reduce a panda dataframe by groups

Question

I've been searching extensively but can't get my head around this issue:

I have a dataframe in pandas that looks like this:

date    ticker Name NoShares SharePrice Volume Relation
2/1/10  aaa    zzz  1        1          1      d 
2/1/10  aaa    yyy  1        2          5      o
2/1/10  aaa    zzz  2        5          2      d  
2/5/10  bbb    xxx  5        5          1      do
2/5/10  ccc    www  5        5          1      d
2/5/10  ccc    www  5        5          1      d
2/5/10  ddd    vvv  5        5          1      o
2/6/10  aaa    zzz  1        1          3      d

Requirements

1. I want to group by date and Name and:
2. have the number of shares summed up
3. have a weighted mean column for the share price (the weights are the NoShares)
4. average the volume and have it as a column
5. Leave relation as it is

so my output would look like this:

date    ticker Name NoShares SharePrice Volume Relation
2/1/10  aaa    zzz  3        3.6        1      d 
2/1/10  aaa    yyy  1        2          5      o
2/5/10  bbb    xxx  5        5          1      do
2/5/10  ccc    www  10       5          1      d
2/5/10  ddd    vvv  5        5          1      o
2/6/10  aaa    zzz  1        1          3      d

I tried the documentation and other answers on stack overflow but don't seem to be able to get it right. Appreciate the help. Cheers.

Answer 1

here's my solution:

grpby = df.groupby(['date','Name'])
a = grpby.apply(lambda x: np.average(a = x['SharePrice'],weights=x['NoShares'])).to_frame(name='SharePrice')
b = grpby.agg({'NoShares':'sum','Volume':'mean','Relation':'max'})
print b.join(a)

             Volume Relation  NoShares  SharePrice
date   Name                                       
2/1/10 yyy   5.0000        o         1      2.0000
       zzz   1.5000        d         3      3.6667
2/5/10 vvv   1.0000        o         5      5.0000
       www   1.0000        d        10      5.0000
       xxx   1.0000       do         5      5.0000
2/6/10 zzz   3.0000        d         1      1.0000

just reset_index() afterwards.